Monitoring animal behaviour and environmental interactions : GPS collars and satellite remote sensing within a satellite remote sensing context

Remote monitoring of animal behaviour in the environment can assist in managing both the animal and its environmental impact. GPS collars which record animal locations with high temporal frequency allow researchers to monitor both animal behaviour and interactions with the environment. These ground-based sensors can be combined with remotely-sensed satellite images to understand animal-landscape interactions. The key to combining these technologies is communication methods such as wireless sensor networks (WSNs). We explore this concept using a case-study from an extensive cattle enterprise in northern Australia and demonstrate the potential for combining GPS collars and satellite images in a WSN to monitor behavioural preferences and social behaviour of cattle.

Monitoring animal behaviour and environmental interactions using wireless sensor networks, GPS collars and satellite remote sensing

Remote monitoring of animal behaviour in the environment can assist in managing both the animal and its environmental impact. GPS collars which record animal locations with high temporal frequency allow researchers to monitor both animal behaviour and interactions with the environment. These ground-based sensors can be combined with remotely-sensed satellite images to understand animal-landscape interactions. The key to combining these technologies is communication methods such as wireless sensor networks (WSNs). We explore this concept using a case-study from an extensive cattle enterprise in northern Australia and demonstrate the potential for combining GPS collars and satellite images in a WSN to monitor behavioural preferences and social behaviour of cattle.

Design and deployment of a remote robust sensor network : experiences from an outdoor water quality monitoring network

This paper investigates a wireless sensor network deployment - monitoring water quality, e.g. salinity and the level of the underground water table - in a remote tropical area of northern Australia. Our goal is to collect real time water quality measurements together with the amount of water being pumped out in the area, and investigate the impacts of current irrigation practice on the environments, in particular underground water salination. This is a challenging task featuring wide geographic area coverage (mean transmission range between nodes is more than 800 meters), highly variable radio propagations, high end-to-end packet delivery rate requirements, and hostile deployment environments. We have designed, implemented and deployed a sensor network system, which has been collecting water quality and flow measurements, e.g., water flow rate and water flow ticks for over one month. The preliminary results show that sensor networks are a promising solution to deploying a sustainable irrigation system, e.g., maximizing the amount of water pumped out from an area with minimum impact on water quality.

Advances in vegetation management for power line corridor monitoring using aerial remote sensing techniques

This paper presents a comprehensive discussion of vegetation management approaches in power line corridors based on aerial remote sensing techniques. We address three issues 1) strategies for risk management in power line corridors, 2) selection of suitable platforms and sensor suite for data collection and 3) the progress in automated data processing techniques for vegetation management. We present initial results from a series of experiments and, challenges and lessons learnt from our project.

Life on the edge : housing experiences in three remote Australian indigenous settlements

This analysis of housing experiences and aspirations in three remote Indigenous settlements in Australia (Mimili, Maningrida and Palm Island) reveals extreme liveability problems directly related to the scale and form of housing provision. Based upon field visits to each of the settlements and extensive interviews with residents and local housing and community officers, the paper analyses two aspects of living in such housing conditions at two spatial scales, the layout of the settlement and the design of individual houses. The failings at both scales are shown to be the fault of a dysfunctional housing system that is only recently been addressed.

Finding the remote: Immersive learning in an outback community

Real-world design education projects present particular challenges when in a place remote from and distinctively different to students’ familiar territory. The teaching challenge is to assist students to translate the skills they learn at university into an entirely new context, facilitating a project they will learn from, and the community will value. In 2008 QUT design and engineering students undertook a project called Linking Karumba for this remote Queensland town. They engaged with a landscape, climate and community dramatically different from their base in urban Brisbane, and in a fortnight produced locally responsive strategic planning options. The theoretical approach to this was twofold: they needed to make a rapid shift along a continuum from being “outsiders” towards becoming “insiders” (Relph 1976), and to create designs responsive to local distinctiveness (Cumberlidge and Musgrave 2007). This paper outlines Linking Karumba’s teaching strategy via an analogy with the “immersion” method in bilingual education. Three teaching methods were adopted. Firstly, the overall framework drew on Brockbank and McGill (1998), and Thomas’ (2006a) approaches to student reflective practice. Within this, Girot’s “Four Trace Concepts” (1999) inspired exercises for finding Karumba and moving toward insideness; and a program of community engagement sought immersion in local distinctiveness, and “conversation” between the differing forms of knowledge and capacities embedded within the community and students (Armstrong 1999, Thomas 2006). The responsiveness of the student work to the character of Karumba’s culture and environment indicated remarkable levels of immersion, and the community highly valued the project outcomes: four strategic planning options which attracted $830 000 in state government funding for implementation.

Applying incremental EM to Bayesian classifiers in the learning of hyperspectral remote sensing data

In this paper, we apply the incremental EM method to Bayesian Network Classifiers to learn and interpret hyperspectral sensor data in robotic planetary missions. Hyperspectral image spectroscopy is an emerging technique for geological investigations from airborne or orbital sensors. Many spacecraft carry spectroscopic equipment as wavelengths outside the visible light in the electromagnetic spectrum give much greater information about an object. The algorithm used is an extension to the standard Expectation Maximisation (EM). The incremental method allows us to learn and interpret the data as they become available. Two Bayesian network classifiers were tested: the Naive Bayes, and the Tree-Augmented-Naive Bayes structures. Our preliminary experiments show that incremental learning with unlabelled data can improve the accuracy of the classifier.